NDLI: Self-tuning measurement fusion Kalman filter and its convergence for multisensor systems with companion form

Content Provider	IEEE Xplore Digital Library
Author	Chenjian Ran Zili Deng
Copyright Year	2010
Description	Author affiliation: Department of Automation, University of Heilongjiang University, Harbin, Xuefu Road 74, China (Chenjian Ran; Zili Deng)
Abstract	For the multisensor systems with companion form and unknown model parameters and noise variances, using recursive instrumental variable(RIV) algorithm, the local and fused model parameter estimators are obtained. Based on the fused model parameter estimators, the information fusion noise variance estimators are presented by using correlation method. They have strong consistence. Further, a self-tuning weighted measurement fusion Kalman filter based on a self-tuning Riccati equation is presented. By the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion Kalman filter in a realization, so that it has asymptotic global optimality. A simulation example applied to signal processing shows its effectiveness.
Starting Page	1712
Ending Page	1717
File Size	820763
Page Count	6
File Format	PDF
ISBN	9781424467129
e-ISBN	9781424467129
DOI	10.1109/WCICA.2010.5554648
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-07-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Kalman filters Weight measurement Noise measurement Mathematical model Convergence Riccati equations Multisensor Information Fusion Paper Measurement Fusion Parameter Estimate Noise Variance Estimator
Content Type	Text
Resource Type	Article

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